623 lines
19 KiB
Python
623 lines
19 KiB
Python
import numpy as np
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from conviction_helpers import get_nodes_by_type, get_edges_by_type, social_affinity_booster, conviction_order
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from bonding_curve_eq import reserve, spot_price
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#import networkx as nx
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from scipy.stats import expon, gamma
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#functions for partial state update block 1
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#Driving processes: arrival of participants, proposals and funds
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##-----------------------------------------
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def gen_new_participant(network, new_participant_holdings):
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i = len([node for node in network.nodes])
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network.add_node(i)
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network.nodes[i]['type']="participant"
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s_rv = np.random.rand()
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network.nodes[i]['sentiment'] = s_rv
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network.nodes[i]['holdings']=new_participant_holdings
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for j in get_nodes_by_type(network, 'proposal'):
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network.add_edge(i, j)
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rv = np.random.rand()
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a_rv = 1-4*(1-rv)*rv #polarized distribution
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network.edges[(i, j)]['affinity'] = a_rv
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network.edges[(i,j)]['tokens'] = a_rv*network.nodes[i]['holdings']
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network.edges[(i, j)]['conviction'] = 0
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network.edges[(i,j)]['type'] = 'support'
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return network
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def gen_new_proposal(network, funds, supply, trigger_func, scale_factor = 1.0/100):
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j = len([node for node in network.nodes])
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network.add_node(j)
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network.nodes[j]['type']="proposal"
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network.nodes[j]['conviction']=0
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network.nodes[j]['status']='candidate'
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network.nodes[j]['age']=0
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rescale = funds*scale_factor
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r_rv = gamma.rvs(3,loc=0.001, scale=rescale)
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network.nodes[j]['funds_requested'] = r_rv
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network.nodes[j]['trigger']= trigger_func(r_rv, funds, supply)
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participants = get_nodes_by_type(network, 'participant')
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proposing_participant = np.random.choice(participants)
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for i in participants:
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network.add_edge(i, j)
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if i==proposing_participant:
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network.edges[(i, j)]['affinity']=1
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else:
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rv = np.random.rand()
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a_rv = 1-4*(1-rv)*rv #polarized distribution
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network.edges[(i, j)]['affinity'] = a_rv
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network.edges[(i, j)]['conviction'] = 0
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network.edges[(i,j)]['tokens'] = 0
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network.edges[(i,j)]['type'] = 'support'
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return network
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def driving_process(params, step, sL, s):
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#placeholder plumbing for random processes
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arrival_rate = 10/(1+s['sentiment'])
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rv1 = np.random.rand()
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new_participant = bool(rv1<1/arrival_rate)
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supporters = get_edges_by_type(s['network'], 'support')
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len_parts = len(get_nodes_by_type(s['network'], 'participant'))
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supply = s['supply']
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expected_holdings = .1*supply/len_parts
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if new_participant:
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h_rv = expon.rvs(loc=0.0, scale=expected_holdings)
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new_participant_holdings = h_rv
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else:
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new_participant_holdings = 0
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network = s['network']
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affinities = [network.edges[e]['affinity'] for e in supporters ]
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median_affinity = np.median(affinities)
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proposals = get_nodes_by_type(network, 'proposal')
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fund_requests = [network.nodes[j]['funds_requested'] for j in proposals if network.nodes[j]['status']=='candidate' ]
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funds = s['funds']
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total_funds_requested = np.sum(fund_requests)
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proposal_rate = 1/median_affinity * (1+total_funds_requested/funds)
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rv2 = np.random.rand()
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new_proposal = bool(rv2<1/proposal_rate)
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sentiment = s['sentiment']
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funds = s['funds']
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scale_factor = funds*sentiment**2/10000
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if scale_factor <1:
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scale_factor = 1
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#this shouldn't happen but expon is throwing domain errors
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if sentiment>.4:
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funds_arrival = expon.rvs(loc = 0, scale = scale_factor )
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else:
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funds_arrival = 0
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return({'new_participant':new_participant,
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'new_participant_holdings':new_participant_holdings,
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'new_proposal':new_proposal,
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'funds_arrival':funds_arrival})
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#Mechanisms for updating the state based on driving processes
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##---
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def update_network(params, step, sL, s, _input):
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network = s['network']
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funds = s['funds']
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supply = s['supply']
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trigger_func = params['trigger_func']
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#print(trigger_func)
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new_participant = _input['new_participant'] #T/F
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new_proposal = _input['new_proposal'] #T/F
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if new_participant:
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new_participant_holdings = _input['new_participant_holdings']
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network = gen_new_participant(network, new_participant_holdings)
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if new_proposal:
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network= gen_new_proposal(network,funds,supply,trigger_func )
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#update age of the existing proposals
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proposals = get_nodes_by_type(network, 'proposal')
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for j in proposals:
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network.nodes[j]['age'] = network.nodes[j]['age']+1
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if network.nodes[j]['status'] == 'candidate':
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requested = network.nodes[j]['funds_requested']
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network.nodes[j]['trigger'] = trigger_func(requested, funds, supply)
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else:
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network.nodes[j]['trigger'] = np.nan
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key = 'network'
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value = network
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return (key, value)
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def increment_funds(params, step, sL, s, _input):
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funds = s['funds']
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funds_arrival = _input['funds_arrival']
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#increment funds
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funds = funds + funds_arrival
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key = 'funds'
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value = funds
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return (key, value)
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def increment_supply(params, step, sL, s, _input):
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supply = s['supply']
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supply_arrival = _input['new_participant_holdings']
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#increment funds
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supply = supply + supply_arrival
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key = 'supply'
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value = supply
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return (key, value)
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def increment_reserve(params, step, sL, s, _input):
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supply = s['supply']
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supply_arrival = _input['new_participant_holdings']
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#increment funds
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supply = supply + supply_arrival
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kappa = params['kappa']
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V0 = params['invariant']
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R = reserve(supply, V0, kappa)
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key = 'reserve'
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value = R
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return (key, value)
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#functions for partial state update block 2
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#Driving processes: completion of previously funded proposals
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##-----------------------------------------
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def check_progress(params, step, sL, s):
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network = s['network']
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proposals = get_nodes_by_type(network, 'proposal')
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completed = []
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failed = []
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for j in proposals:
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if network.nodes[j]['status'] == 'active':
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grant_size = network.nodes[j]['funds_requested']
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base_completion_rate=params['base_completion_rate']
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likelihood = 1.0/(base_completion_rate+np.log(grant_size))
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base_failure_rate = params['base_failure_rate']
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failure_rate = 1.0/(base_failure_rate+np.log(grant_size))
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if np.random.rand() < likelihood:
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completed.append(j)
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elif np.random.rand() < failure_rate:
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failed.append(j)
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return({'completed':completed, 'failed':failed})
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#Mechanisms for updating the state based on check progress
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##---
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def complete_proposal(params, step, sL, s, _input):
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network = s['network']
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participants = get_nodes_by_type(network, 'participant')
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proposals = get_nodes_by_type(network, 'proposal')
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competitors = get_edges_by_type(network, 'conflict')
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completed = _input['completed']
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for j in completed:
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network.nodes[j]['status']='completed'
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for c in proposals:
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if (j,c) in competitors:
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conflict = network.edges[(j,c)]['conflict']
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for i in participants:
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network.edges[(i,c)]['affinity'] = network.edges[(i,c)]['affinity'] *(1-conflict)
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for i in participants:
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force = network.edges[(i,j)]['affinity']
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sentiment = network.nodes[i]['sentiment']
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network.nodes[i]['sentiment'] = get_sentimental(sentiment, force, decay=0)
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failed = _input['failed']
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for j in failed:
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network.nodes[j]['status']='failed'
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for i in participants:
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force = -network.edges[(i,j)]['affinity']
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sentiment = network.nodes[i]['sentiment']
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network.nodes[i]['sentiment'] = get_sentimental(sentiment, force, decay=0)
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key = 'network'
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value = network
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return (key, value)
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def update_sentiment_on_completion(params, step, sL, s, _input):
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network = s['network']
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proposals = get_nodes_by_type(network, 'proposal')
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completed = _input['completed']
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failed = _input['failed']
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grants_outstanding = np.sum([network.nodes[j]['funds_requested'] for j in proposals if network.nodes[j]['status']=='active'])
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grants_completed = np.sum([network.nodes[j]['funds_requested'] for j in completed])
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grants_failed = np.sum([network.nodes[j]['funds_requested'] for j in failed])
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sentiment = s['sentiment']
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if grants_outstanding>0:
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force = (grants_completed-grants_failed)/grants_outstanding
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else:
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force=1
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mu = params['sentiment_decay']
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if (force >=0) and (force <=1):
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sentiment = get_sentimental(sentiment, force, mu)
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else:
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sentiment = get_sentimental(sentiment, 0, mu)
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key = 'sentiment'
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value = sentiment
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return (key, value)
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def get_sentimental(sentiment, force, decay=0):
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mu = decay
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sentiment = sentiment*(1-mu) + force
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if sentiment > 1:
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sentiment = 1
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return sentiment
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#functions for partial state update block 3
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#Decision processes: trigger function policy
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##-----------------------------------------
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def trigger_function(params, step, sL, s):
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network = s['network']
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funds = s['funds']
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supply = s['supply']
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proposals = get_nodes_by_type(network, 'proposal')
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tmin = params['tmin']
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trigger_func = params['trigger_func']
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accepted = []
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triggers = {}
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funds_to_be_released = 0
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for j in proposals:
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if network.nodes[j]['status'] == 'candidate':
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requested = network.nodes[j]['funds_requested']
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age = network.nodes[j]['age']
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threshold = trigger_func(requested, funds, supply)
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if age > tmin:
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conviction = network.nodes[j]['conviction']
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if conviction >threshold:
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accepted.append(j)
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funds_to_be_released = funds_to_be_released + requested
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else:
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threshold = np.nan
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triggers[j] = threshold
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#catch over release and keep the highest conviction results
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if funds_to_be_released > funds:
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#print('funds ='+str(funds))
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#print(accepted)
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ordered = conviction_order(network, accepted)
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#print(ordered)
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accepted = []
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release = 0
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ind = 0
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while release + network.nodes[ordered[ind]]['funds_requested'] < funds:
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accepted.append(ordered[ind])
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release= network.nodes[ordered[ind]]['funds_requested']
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ind=ind+1
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return({'accepted':accepted, 'triggers':triggers})
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#functions for partial state update block 3
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#state updates
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##---
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def decrement_funds(params, step, sL, s, _input):
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funds = s['funds']
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network = s['network']
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accepted = _input['accepted']
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#decrement funds
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for j in accepted:
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funds = funds - network.nodes[j]['funds_requested']
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key = 'funds'
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value = funds
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return (key, value)
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def update_proposals(params, step, sL, s, _input):
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network = s['network']
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accepted = _input['accepted']
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triggers = _input['triggers']
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participants = get_nodes_by_type(network, 'participant')
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proposals = get_nodes_by_type(network, 'proposals')
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sensitivity = params['sensitivity']
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for j in proposals:
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network.nodes[j]['trigger'] = triggers[j]
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#bookkeeping conviction and participant sentiment
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for j in accepted:
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network.nodes[j]['status']='active'
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network.nodes[j]['conviction']=np.nan
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#change status to active
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for i in participants:
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#operating on edge = (i,j)
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#reset tokens assigned to other candidates
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network.edges[(i,j)]['tokens']=0
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network.edges[(i,j)]['conviction'] = np.nan
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#update participants sentiments (positive or negative)
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affinities = [network.edges[(i,p)]['affinity'] for p in proposals if not(p in accepted)]
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if len(affinities)>1:
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max_affinity = np.max(affinities)
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force = network.edges[(i,j)]['affinity']-sensitivity*max_affinity
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else:
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force = 0
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#based on what their affinities to the accepted proposals
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network.nodes[i]['sentiment'] = get_sentimental(network.nodes[i]['sentiment'], force, False)
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key = 'network'
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value = network
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return (key, value)
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def update_sentiment_on_release(params, step, sL, s, _input):
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network = s['network']
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proposals = get_nodes_by_type(network, 'proposal')
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accepted = _input['accepted']
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proposals_outstanding = np.sum([network.nodes[j]['funds_requested'] for j in proposals if network.nodes[j]['status']=='candidate'])
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proposals_accepted = np.sum([network.nodes[j]['funds_requested'] for j in accepted])
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sentiment = s['sentiment']
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force = proposals_accepted/proposals_outstanding
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if (force >=0) and (force <=1):
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sentiment = get_sentimental(sentiment, force, False)
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else:
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sentiment = get_sentimental(sentiment, 0, False)
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key = 'sentiment'
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value = sentiment
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return (key, value)
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#functions for partial state update block 4
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#Decision processes: trigger function policy
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##---
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def participants_decisions(params, step, sL, s):
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network = s['network']
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participants = get_nodes_by_type(network, 'participant')
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proposals = get_nodes_by_type(network, 'proposal')
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candidates = [j for j in proposals if network.nodes[j]['status']=='candidate']
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sensitivity = params['sensitivity']
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gain = .01
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delta_holdings={}
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proposals_supported ={}
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for i in participants:
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engagement_rate = .3*network.nodes[i]['sentiment']
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if np.random.rand()<engagement_rate:
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force = network.nodes[i]['sentiment']-sensitivity
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delta_holdings[i] = network.nodes[i]['holdings']*gain*force
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support = []
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for j in candidates:
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booster = social_affinity_booster(network, j, i)
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#print(booster)
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affinity = network.edges[(i, j)]['affinity']+booster
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cutoff = sensitivity*np.max([network.edges[(i,p)]['affinity'] for p in candidates])
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if cutoff <.5:
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cutoff = .5
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if affinity > cutoff:
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support.append(j)
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proposals_supported[i] = support
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else:
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delta_holdings[i] = 0
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proposals_supported[i] = [j for j in candidates if network.edges[(i,j)]['tokens']>0 ]
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return({'delta_holdings':delta_holdings, 'proposals_supported':proposals_supported})
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#functions for partial state update block 4
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#state updates
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##---
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def update_tokens(params, step, sL, s, _input):
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network = s['network']
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delta_holdings = _input['delta_holdings']
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proposals = get_nodes_by_type(network, 'proposal')
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candidates = [j for j in proposals if network.nodes[j]['status']=='candidate']
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proposals_supported = _input['proposals_supported']
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participants = get_nodes_by_type(network, 'participant')
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alpha = params['alpha']
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min_support = params['min_supp']
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for i in participants:
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network.nodes[i]['holdings'] = network.nodes[i]['holdings']+delta_holdings[i]
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supported = proposals_supported[i]
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total_affinity = np.sum([ network.edges[(i, j)]['affinity'] for j in supported])
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for j in candidates:
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if j in supported:
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normalized_affinity = network.edges[(i, j)]['affinity']/total_affinity
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network.edges[(i, j)]['tokens'] = normalized_affinity*network.nodes[i]['holdings']
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else:
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network.edges[(i, j)]['tokens'] = 0
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prior_conviction = network.edges[(i, j)]['conviction']
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current_tokens = network.edges[(i, j)]['tokens']
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network.edges[(i, j)]['conviction'] =current_tokens+alpha*prior_conviction
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for j in candidates:
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network.nodes[j]['conviction'] = np.sum([ network.edges[(i, j)]['conviction'] for i in participants])
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total_tokens = np.sum([network.edges[(i, j)]['tokens'] for i in participants ])
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if total_tokens < min_support:
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network.nodes[j]['status'] = 'killed'
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key = 'network'
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value = network
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return (key, value)
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#organizing the bonding curve into a nested state would
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#make this code more efficient, lots of duplicated logic here
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def update_supply(params, step, sL, s, _input):
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supply = s['supply']
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delta_holdings = _input['delta_holdings']
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delta_supply = np.sum([v for v in delta_holdings.values()])
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supply = supply + delta_supply
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key = 'supply'
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value = supply
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return (key, value)
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def update_reserve(params, step, sL, s, _input):
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supply = s['supply']
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delta_holdings = _input['delta_holdings']
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delta_supply = np.sum([v for v in delta_holdings.values()])
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supply = supply + delta_supply
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kappa = params['kappa']
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V0 = params['invariant']
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#print("kappa="+str(kappa))
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R = reserve(supply, V0, kappa)
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key = 'reserve'
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value = R
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return (key, value)
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def update_price(params, step, sL, s, _input):
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supply = s['supply']
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delta_holdings = _input['delta_holdings']
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delta_supply = np.sum([v for v in delta_holdings.values()])
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supply = supply + delta_supply
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kappa = params['kappa']
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V0 = params['invariant']
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R = reserve(supply, V0, kappa)
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price = spot_price(R, V0, kappa)
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key = 'spot_price'
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value = price
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return (key, value)
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def update_funds(params, step, sL, s, _input):
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supply = s['supply']
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delta_holdings = _input['delta_holdings']
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minus_supply = np.sum([v for v in delta_holdings.values() if v<0])
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#print(minus_supply)
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min_supply = supply + minus_supply
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kappa = params['kappa']
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V0 = params['invariant']
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old_R = reserve(supply, V0, kappa)
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min_R = reserve(min_supply, V0, kappa)
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exit_tax = params['tax_rate']
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funds = s['funds']+exit_tax*(old_R-min_R)
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key = 'funds'
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value = funds
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return (key, value)
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def pad(vec, length,fill=True):
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if fill:
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padded = np.zeros(length,)
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else:
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padded = np.empty(length,)
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padded[:] = np.nan
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for i in range(len(vec)):
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padded[i]= vec[i]
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return padded
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def make2D(key, data, fill=False):
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maxL = data[key].apply(len).max()
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newkey = 'padded_'+key
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data[newkey] = data[key].apply(lambda x: pad(x,maxL,fill))
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reshaped = np.array([a for a in data[newkey].values])
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return reshaped |